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Scientific Contributions Oil & Gas, Vol. 44. No. 3, December 2021: 183 - 198
produce the hydrocarbon reserves while taking into account both technical and economic aspects (Mustapha, et al., 2012).
In the scenario of low oil prices and strict rules and regulations for the extraction of hydrocarbon, the world’s market for oil and gas increased, as well as a large set of challenges for oil and gas companies that are in the period of exploration and development.
The main challenges facing the oil and gas industries are mainly cost reduction, the introduction of cutting-edge innovative technology, exploring more hydrocarbons in the sector while maintaining stability between supply and demand, extending the lives of sites already discovered, and discovering new prospects for replacing the depleted oil and gas reserves (Haider, 2020).
The country of Yemen has a proven oil reserve value of 3 billion barrels and it produces oil around 127,000 barrels per day (BPD). (GeoExpro, 2016), However, oil production continues to decline as most of Yemen’s oil fields have unconventional reservoirs such as high viscous oil and tight formations with low permeability. The decrease in oil production was also attributed to the low level of oil and gas exploration activities due to civil wars, lack of government participation in exploration activities (GeoExpro, 2016), and the non-existing application of advanced recovery technology in Yemeni oil fields.
Heavy oil is defined as crude oil with high specific gravity at reservoir conditions and usually having high viscosity. Due to high viscosity values, it is hard to maintain flow of heavy oil in the wellbore.
Primary recoveries from these fields are lower than in the cases of light oil reservoirs. Production from heavy oil fields mostly requires techniques known as thermal enhanced oil recovery (EOR) methods.
These methods are complex and require screening of the rock, fluid, and additional field characteristics for their suitability (Taber, et al., 1997).
Steam flooding (SF) as a conventional thermal EOR method has been applied in many heavy-oil fields around the world. This technique is also referred to as continuous steam injection or stream- drive. In this process, steam is constantly injected through the injection well, while oil is produced through different wells. The steam pushes oil toward the producing well by reducing its viscosity, which improves its mobility ratio and, consequently, its displacement and areal sweep efficiency (Green &
Willhite, 2018).
The Field “AR” selected for this study is a newly developed field located in Eastern Yemen with an Original Oil in Place (OOIP) value of 62 million barrels. The relatively-thin sandstone reservoirs are dominant at moderate depths and the reservoir oil is highly under-saturated, API gravity of 14.2 with a very low solution gas-oil ratio, initial oil viscosity is 420 cP and initial reservoir pressure of about 1125 psi. It is naturally producing using energy from the aquifer, even though, this aquifer poses a disadvantage due to high increase in water cut. Over time, the oil production keeps decreasing and a development plan is needed to increase the oil recovery.
Before applying the steam flood EOR method, an in-depth study should be undertaken to find out the appropriate and optimum steam flood injection parameters. This study aims to optimize steam flood injection in the field by creating reservoir simulation scenarios, that include injection well’s location, number of injection wells, injection pattern, injection rate, steam quality, injection pressure, and temperature needed to increase the oil recovery. And finally, economic studies are needed to play a role in determining the optimum and economic development scenario for the field (Elbaloula, et al., 2020).
However, the method that has been done so far as in Sudanese Oil Field Fula North East and many others is by trial error for all available possibilities, such as a combination of wells location, number of production and injection wells, and the required injection rates (Elbaloula, et al., 2020). This process consumes time. And in this study, the field development optimization study was carried out using the Stochastic approach method that processes simultaneous simulations with various possibilities by using the optimum simulation result from the deterministic approach to determine the most influencing parameter in the steam flood process as well as to optimize the heavy oil field development plan using steam flooding (Temizel, et al., 2016). The results of the stochastic approach are expected to be far more accurate, optimum, and faster to obtain than the deterministic approach.
DAtA AND MEtHODS
This research is carried out using a data processing flow as shown on Figure 1. The simulator used is the computer modeling group (CMG) Stars for the steam flooding injection scenarios, while the stochastic approach optimization using CMOST DECE.
185 Feasibility Study and Technical Optimization by Implementing Steam Flooding for the Field
Development Plan of A Heavy-Oil Field in Yemen (Mohammed Sheikh Salem Al-Attas and Amega Yasutra)
At the initial stage, field data preparation is carried out such as geological data, fluid and rock data, available well data for making simulation models.
Next, a thermal Base Case model is made for the current field conditions. And before the process development is carried out, it is necessary to evaluate whether the production rate for the available wells and the perforation zone is optimum or not. The development process begins with determining the
number of infill wells. And that is done by looking for potential areas with potential intervals. Then evaluate the steam injection parameters to determine the most optimal parameters. Several optimization scenarios are made manually by adding infill and injection wells which will give cumulative value of oil production. And at the same time optimization is done by using CMOST. The optimization results obtained are compared between the two methods.
Figure 1 Workflow of the study.
Title of Your Paper, First Author et al. / Scientific Contributions Oil and Gas
until it reaches the optimum condition for the objective function sought.
Start
CMG STARS Reservoir Simulation Model Creation
Initialization Base Case Model Data Preparation for Building Simulation Model Geological
(Rescue Model) Fluid Properties
(PVT Data) Rock Properties
(SCAL) Wells Location
& Completion
Evaluate Existing Production Wells
Optimum Production Rate Redesign Well Perforation
Development Scenarios (Reservoir Simulations)
Infill Wells Injection Wells Prospect Area
Prospect Interval
Steam Injection Rate Steam Temperature
Steam Quality Injection Duration Deterministic Optimization
Stochastic Optimization
Economic Analysis Finish
Injection Depth Injection Pressure Oil Cumulative Production Rate
(Recovery Factor)
Figure 1
Workflow of the Study.
III. FIELD OVERVIEW
This contains descriptions of Field “X: which include descriptions of the existing wells in the field, reservoir model, EOR screening result, infill wells optimization, sensitivity analysis of steam injection parameters.
A. Field Description
Field “X” is located in eastern Yemen. It is medium in size with a productive area of approximately 1672 acres with three sandstone formations, the S1, S2, and S3 reservoirs. The field was discovered in March 2004 by an exploration well. The Exploration well penetrated the formation and the testing indicated the high oil saturation. Current development and production activities are focused on the S1 sand reservoir, mainly due to the more favorable reservoir characteristics compared to the S2 and S3 sand reservoir and to avoid excessive water production.
A total of 9 wells were drilled in Field “X”
but only 6 of them have been producing oil as shown in Figure 2. The sandstone reservoir contains highly under-saturated, 14.2 API gravity oil with a low solution gas-oil ratio (1.7 Scf/Bbl) and reservoir oil viscosity of 472 cP. Reservoir pressure had a slight decline due to the connection with the bottom aquifer beneath the Water-Oil Contact (WOC).
Figure 2
Depth Structure Map of Field “X” Reservoir.
B. Production History
Oil production started in December 2009 with initial production rates in the range of 250 - 400 barrels per day for each of the wells that produced oil in the field. The Producing wells showed a little water production during the initial stages but in the end, water production begins to increase after a few months. The total field production rate reached a peak of production 354 MSTB during 2012 as shown in Figure 3. The initial hydrostatic reservoir pressure in the reservoir is 1125 psi at a datum of 1072 meters.
The relatively small decline in reservoir pressure demonstrates the great strength of the aquifer.
This field has been in production for a short period and has been closed for 8 years up until the end of the year 2020, therefore the production Start
Data Preparation for Building Simulation Model
Geological
(Rescue Model) Fluid Properties
(PVT Data) Rock Properties
(SCAL) Wells Location
& Completion
CMG STARS Reservoir Simulation Model Creation
Initialization Base Case Model Optimum Production Rate Evaluate Existing
Production Wells Redesign Well Perforation
Development Scenarios (Reservoir Simulations)
Finish Economic Analysis Oil Cumulative Production Rate
(Recovery Factor) Determination Optimization Stochastic Optimization
Prospect Interval Prospect Area
Infill Wells Injection Wells
Steam Injection Rate Steam Temperature
Steam Quality Injection Duration
Injection Depth Injection Pressure
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Scientific Contributions Oil & Gas, Vol. 44. No. 3, December 2021: 183 - 198
In conducting the reservoir simulations, some limits for each production and injection wells have been determined, such as number of infill wells and injected steam quality and temperature. However, the total oil production rate in the field, the number of injection wells, steam injection rate, steam injection pressure are not limited and are expected to be optimized as an output from this research.
The simulation results will be evaluated whether it can increase the recovery factor and the produced cumulative oil, then economic calculations are carried out from the simulation results. Economic calculations can provide results in the form of cash flow, net present value (NPV), and will be a consideration for making decisions to invest in the field “AR”
development project.
After simulations for many scenarios of cases and economic results are obtained using deterministic analysis, the optimum case is selected as the field’s development plan based on the optimum results. This field development scenario case is then optimized further using the stochastic optimization method. In this study, the stochastic optimization method uses maximum recovery factor and maximum net present value as the objective functions.
This study aims to find the range of uncertainty based on the output of the stochastic method with the objective function of the maximum recovery factor (RF) and maximum net present value (NPV) value. The input parameters used in this research are the location of infill and injection wells, number of
injection wells, steam injection rate, and pressure.
After determining the input limits, the particles in the CMOST result chart will move at a certain speed and the objective function value will be sought at each position until it reaches the optimum condition for the objective function sought.
A. Field Overview
This part contains descriptions of Field “AR”:
which include descriptions of the existing wells in the field, reservoir model, EOR screening result, infill wells optimization, sensitivity analysis of steam injection parameters.
1. Field Description
Field “AR” is located in eastern Yemen. It is medium in size with a productive area of approximately 1672 acres with three sandstone reservoirs, the S1, S2, and S3. The field was discovered in March 2004 by an exploration well. The Exploration well penetrated the formation and the testing indicated high oil saturation. Current development and production activities are focused on the S1 sand reservoir, mainly due to the more favorable reservoir characteristics compared to the S2 and S3 reservoirs and to avoid excessive water production.
A total of nine wells were drilled in “AR” Field but only six of them have been producing oil as shown on Figure 2. The sandstone reservoir contains highly under-saturated, 14.2 API gravity oil with a low solution gas-oil ratio (1.7 Scf/Bbl) and reservoir
Figure 2
Depth structure map of “AR” field reservoir.
Title of Your Paper, First Author et al. / Scientific Contributions Oil and Gas
until it reaches the optimum condition for the objective function sought.
Start
CMG STARS Reservoir Simulation Model Creation
Initialization Base Case Model Data Preparation for Building Simulation Model Geological
(Rescue Model) Fluid Properties
(PVT Data) Rock Properties
(SCAL) Wells Location
& Completion
Evaluate Existing Production Wells
Optimum Production Rate Redesign Well Perforation Development Scenarios
(Reservoir Simulations)
Infill Wells Injection Wells Prospect Area
Prospect Interval
Steam Injection Rate Steam Temperature
Steam Quality Injection Duration Deterministic Optimization
Stochastic Optimization
Economic Analysis Finish
Injection Depth Injection Pressure Oil Cumulative Production Rate
(Recovery Factor)
Figure 1
Workflow of the Study.
III. FIELD OVERVIEW
This contains descriptions of Field “X: which include descriptions of the existing wells in the field, reservoir model, EOR screening result, infill wells optimization, sensitivity analysis of steam injection parameters.
A. Field Description
Field “X” is located in eastern Yemen. It is medium in size with a productive area of approximately 1672 acres with three sandstone formations, the S1, S2, and S3 reservoirs. The field was discovered in March 2004 by an exploration well. The Exploration well penetrated the formation and the testing indicated the high oil saturation. Current development and production activities are focused on the S1 sand reservoir, mainly due to the more favorable reservoir characteristics compared to the S2 and S3 sand reservoir and to avoid excessive water production.
A total of 9 wells were drilled in Field “X”
but only 6 of them have been producing oil as shown in Figure 2. The sandstone reservoir contains highly under-saturated, 14.2 API gravity oil with a low solution gas-oil ratio (1.7 Scf/Bbl) and reservoir oil viscosity of 472 cP. Reservoir pressure had a slight decline due to the connection with the bottom aquifer beneath the Water-Oil Contact (WOC).
Figure 2
Depth Structure Map of Field “X” Reservoir.
B. Production History
Oil production started in December 2009 with initial production rates in the range of 250 - 400 barrels per day for each of the wells that produced oil in the field. The Producing wells showed a little water production during the initial stages but in the end, water production begins to increase after a few months. The total field production rate reached a peak of production 354 MSTB during 2012 as shown in Figure 3. The initial hydrostatic reservoir pressure in the reservoir is 1125 psi at a datum of 1072 meters.
The relatively small decline in reservoir pressure demonstrates the great strength of the aquifer.
This field has been in production for a short
period and has been closed for 8 years up until
the end of the year 2020, therefore the production
187 Feasibility Study and Technical Optimization by Implementing Steam Flooding for the Field
Development Plan of A Heavy-Oil Field in Yemen (Mohammed Sheikh Salem Al-Attas and Amega Yasutra)
oil viscosity of 472 cP. Reservoir pressure had a slight decline due to the connection with the bottom aquifer beneath the water-oil contact (WOC).
2. Production History
Oil production started in December 2009 with initial production rates in the range of 250 - 400 barrels per day for each of the wells that produced oil in the field. The Producing wells showed a little water production during the initial stages but in the end, water production begins to increase after a few months. The total field production rate reached peak of production 354 MSTB during 2012 as shown on Figure 3. The initial hydrostatic reservoir pressure in the reservoir is 1125 psi at a datum of 1072 meters.
The relatively small decline in reservoir pressure demonstrates the great strength of the aquifer. This field has been in production for a short period and has been closed for 8 years up until the end of the year 2020, therefore the production data cannot be used as history matching reference data.
3. Reservoir Simulation Model
The 3D grid for the dynamic model of the sandstone reservoir is oriented along the faults. The average value of the cell height is 0.4m. The grid dimension is 93*128*110. Total grid blocks are 1,309,440 of which 578,054 are active cells. In this
study, the grid size was upscaled in the Z-axis using a CMG algorithm that cuts the grid according to its rock type. The results obtained are almost exactly as results from the original grid size. Figure 4 shows the porosity distribution in “AR” field.
All required properties distribution (including elevation, bulk volumes, net to gross, porosity, horizontal permeability, vertical permeability, and water saturation) are exported directly from the fixed static model to the simulation dynamic model.
Initial reservoir pressure is calculated for all grid blocks based on fluid gradient, elevations, and datum pressure.
The data input of relative permeability tables is mandatory for reservoir modeling, and this information is one of the most important factors influencing the recovery factor and water cut dynamics obtained from the model. Unfortunately, there’s no special core analysis (relative permeability, residual saturation, and capillary pressures for oil, water, and gas) for the sandstone reservoir. Core analysis of “AR” field core provided only the “porosity - permeability”
relationship. Therefore, experimental data from a similar field that has similar properties to the “AR”
field sandstone reservoir was used to fill the missing relative permeability data and constructs the geological model.
Figure 3
History of production in “AR” Field.
First Author et al. / Mechatronics, Electrical Power, and Vehicular Technology XX (20XX) XX-XX
data cannot be used as History Matching reference data.
Figure 3
History of production in Field “X”.
C. Reservoir Simulation Model
The 3D grid for the dynamic model of the sandstone reservoir is oriented along the faults.
The average value of the cell height is 0.4m. The Grid dimension is 93*128*110. Total grid blocks are 1,309,440 of which 578,054 are active cells.
In this study, the grid size was upscaled in the Z- axis using a CMG algorithm that cuts the grid according to its rock type. The results obtained are almost exactly as results from the original grid size. Figure 4 shows the porosity distribution in Field “X”.
All required properties distribution (including elevation, bulk volumes, NTG, porosity, horizontal permeability, vertical permeability, and water saturation) are exported directly from the fixed model to the simulation model. Initial reservoir pressure is calculated for all grid blocks based on fluid gradient, elevations, and datum pressure.
The data input of relative permeability tables is mandatory for reservoir modeling, and this information is one of the most important factors influencing the recovery factor and water cut dynamics obtained from the model.
Unfortunately, there’s no special core analysis (relative permeability, residual saturation, and capillary pressures for oil, water, and gas) for the sandstone reservoir. Core analysis of Field “X”
core provided only the “porosity - permeability”
relationship. Therefore, experimental data from a similar field that has similar properties to the Field “X” sandstone reservoir was used to construct the geological model.
The common approach to reservoir modeling provides that the geological model is upscaled before the reservoir model starts to be saturated with PVT, rock ‐ fluid, and other production data.
The goal is to reduce the run times since geological models usually have quite many grid blocks and run times on such grids are high decreasing the value of the models. Upscaling can resolve this issue with very little decrease in accuracy and allow running multiple scenarios within a limited time. With this idea in the background, the geological model of Field “X”
was upscaled vertically only using the arithmetical averaging algorithm. As a result, the reduced active grid blocks from 578 thousand to 332 thousand.
Figure 4
3D View of Porosity Distribution.
D. Optimization Of Vertical Well Placement The profitability of an Oil Field development project highly depends on the selection of appropriate production well locations. The conventional method, called trial and error, is to manually determine well locations to seek the highest recovery after reservoir simulation.
However, the conventional trial and error method might be more complicated in multilayer reservoirs. Therefore, in this study Simulation Opportunity Index was used to determine the best infill well locations.
Trial and error method
Field “X” conducted a previous study to determine the best infill well location using the trial-and-error method. For over 150 infills well location were proposed with a spacing of 250 meters between each well. The model was run separately in the base case model to determine which well produces the highest recovery factor.
How this method of optimization requires time and the result may not be accurate when multiple infill wells produce at the same time that will affect the performance of one well to another.
The locations of the proposed wells in this grid
from the previous development plan are shown in
Figure 5.
188
Scientific Contributions Oil & Gas, Vol. 44. No. 3, December 2021: 183 - 198
The common approach to reservoir modeling provides that the geological model is upscaled before the reservoir model starts to be saturated with PVT, rock‐fluid, and other production data.
The goal is to reduce the run times since geological models usually have quite many grid blocks and run times on such grids are long hence decreasing the validity of the models. Upscaling can resolve this issue with very little decrease in accuracy and allow running multiple scenarios within a limited time.
With this idea in the background, the geological model of “AR” field was upscaled vertically only using the arithmetical averaging algorithm. This results in reducing active grid blocks from 578 thousand to 332 thousand.
4. Optimization Of Vertical Well Placement Profitability of an oil field development project highly depends on selection of appropriate production well locations. The conventional method uses trial and error, is to manually determine well locations to seek the highest recovery after reservoir simulation.
However, the conventional trial and error method might be more complicated in multilayer reservoirs.
Therefore, in this study simulation opportunity index (SOI) was used to determine the best infill well locations.
a. trial and error method
A previous study was conducted for “AR” field to determine the best infill well location using the trial-and-error method (“AR” field POD 2013). For over 150 infills well location were proposed with a spacing of 250 meters between wells. The model was run separately in the base case model to determine which well produces the highest recovery factor.
How this method of optimization requires time and the result may not be accurate when multiple infill wells produce at the same time that will affect the performance of one well to another. The locations of the proposed wells in this grid from the previous development plan are shown on Figure 5.
b. Simulation Opportunity Index (SOI) Analysis The simulation opportunity index (SOI) method is applied for “AR” field using the Petrel simulator.
The result of the SOI evaluation and the proposed infill well candidates are shown on Figure 6. The red zone represents the area with the highest oil potential and is recommended for drilling in those zones. The Infill wells were selected from the previous plan of development locations and those wells are also planned to be used for future injection.
Figure 4
3D view of porosity distribution.
First Author et al. / Mechatronics, Electrical Power, and Vehicular Technology XX (20XX) XX-XX
data cannot be used as History Matching reference data.
Figure 3
History of production in Field “X”.
C. Reservoir Simulation Model
The 3D grid for the dynamic model of the sandstone reservoir is oriented along the faults.
The average value of the cell height is 0.4m. The Grid dimension is 93*128*110. Total grid blocks are 1,309,440 of which 578,054 are active cells.
In this study, the grid size was upscaled in the Z- axis using a CMG algorithm that cuts the grid according to its rock type. The results obtained are almost exactly as results from the original grid size. Figure 4 shows the porosity distribution in Field “X”.
All required properties distribution (including elevation, bulk volumes, NTG, porosity, horizontal permeability, vertical permeability, and water saturation) are exported directly from the fixed model to the simulation model. Initial reservoir pressure is calculated for all grid blocks based on fluid gradient, elevations, and datum pressure.
The data input of relative permeability tables is mandatory for reservoir modeling, and this information is one of the most important factors influencing the recovery factor and water cut dynamics obtained from the model.
Unfortunately, there’s no special core analysis (relative permeability, residual saturation, and capillary pressures for oil, water, and gas) for the sandstone reservoir. Core analysis of Field “X”
core provided only the “porosity - permeability”
relationship. Therefore, experimental data from a similar field that has similar properties to the Field “X” sandstone reservoir was used to construct the geological model.
The common approach to reservoir modeling provides that the geological model is upscaled before the reservoir model starts to be saturated with PVT, rock ‐ fluid, and other production data.
The goal is to reduce the run times since geological models usually have quite many grid blocks and run times on such grids are high decreasing the value of the models. Upscaling can resolve this issue with very little decrease in accuracy and allow running multiple scenarios within a limited time. With this idea in the background, the geological model of Field “X”
was upscaled vertically only using the arithmetical averaging algorithm. As a result, the reduced active grid blocks from 578 thousand to 332 thousand.
Figure 4
3D View of Porosity Distribution.
D. Optimization Of Vertical Well Placement The profitability of an Oil Field development project highly depends on the selection of appropriate production well locations. The conventional method, called trial and error, is to manually determine well locations to seek the highest recovery after reservoir simulation.
However, the conventional trial and error method might be more complicated in multilayer reservoirs. Therefore, in this study Simulation Opportunity Index was used to determine the best infill well locations.
Trial and error method
Field “X” conducted a previous study to determine the best infill well location using the trial-and-error method. For over 150 infills well location were proposed with a spacing of 250 meters between each well. The model was run separately in the base case model to determine which well produces the highest recovery factor.
How this method of optimization requires time and the result may not be accurate when multiple infill wells produce at the same time that will affect the performance of one well to another.
The locations of the proposed wells in this grid from the previous development plan are shown in Figure 5.
0.31 0.28 0.25 0.22 0.19 0.16 0.13 0.09 0.06 0.03 0.00
189 Feasibility Study and Technical Optimization by Implementing Steam Flooding for the Field
Development Plan of A Heavy-Oil Field in Yemen (Mohammed Sheikh Salem Al-Attas and Amega Yasutra)
Figure 5
Trial and error simulation method.
Figure 6
Infill wells location using SOI method on “AR” field top structure map.
Title of Your Paper, First Author et al. / Scientific Contributions Oil and Gas
Figure 5
Trial and Error Simulation Method.
Simulation Opportunity Index (SOI) Analysis The Simulation Opportunity Index method was applied For field "X" using the Petrel simulator. The result of the SOI evaluation and the proposed infill well candidates are shown in Figure 6. The red zone represents the area with the highest oil potential and is recommended to drill in those zones. The Infill wells were selected from the previous POD plan location and those wells are also planned to be used for future injection.
Figure 6
Average Map (SOI) for Infill Wells.
IV. RESULTS AND DISCUSSION
Field “X” development plan optimization was carried out starting with the determination of the optimum number of infill wells. Afterward, steam injection scenarios were added with a different number of injection wells. Then an economic calculation is carried out that will produce the economic parameter values (NPV) of each case scenario. Once the optimum scenario had been determined using the manual method (Deterministic Approach), the Stochastic Approach is conducted to further optimize the
field development plan with the objective functions.
A. Infill Wells Optimization (Deterministic Approach)
The addition of infill wells in this study was carried out by looking at the distribution of locations in the field with the Simulation Opportunity Index (SOI) that was calculated in the previous chapter. The addition of infill drilling wells was carried out one by one and the additional recovery factor (RF) and the cumulative oil production were observed as showed in Table 1.
Table 1
The Recovery Factor for Number of Infill Wells.
Scenario
Production Oil Rate @ 2041,
(STB/D)
Cumulative Production
Rate, (MMSTB)
Recovery Factor,
(%) Base Case
(6 Wells) 807 9.59 15.46%
Case 1
(+1 Infill Well) 686 10.82 17.46%
Case 2
(+2 Infill Wells) 662 12.00 19.35%
Case 3
(+3 Infill Wells) 606 12.83 20.69%
Case 4
(+4 Infill Wells) 546 13.24 21.36%
Case 5
(+5 Infill Wells) 494 13.66 22.03%
Case 6
(+6 Infill Wells) 463 13.97 22.54%
Case 7
(+7 Infill Wells) 421 14.08 22.71%
Case 8
(+8 Infill Wells) 397 14.10 22.74%
After the production forecasting simulation is carried out for 20 years, the result for the production profile of the cumulative oil, oil rate, and water production can be seen after increasing the number of infill wells in the Figures 7 and 8.
Figure 7
Cumulative Oil Vs Time (Infill Wells).
Title of Your Paper, First Author et al. / Scientific Contributions Oil and Gas
Figure 5
Trial and Error Simulation Method.
Simulation Opportunity Index (SOI) Analysis The Simulation Opportunity Index method was applied For field "X" using the Petrel simulator. The result of the SOI evaluation and the proposed infill well candidates are shown in Figure 6. The red zone represents the area with the highest oil potential and is recommended to drill in those zones. The Infill wells were selected from the previous POD plan location and those wells are also planned to be used for future injection.
Figure 6
Average Map (SOI) for Infill Wells.
IV. RESULTS AND DISCUSSION
Field “X” development plan optimization was carried out starting with the determination of the optimum number of infill wells. Afterward, steam injection scenarios were added with a different number of injection wells. Then an economic calculation is carried out that will produce the economic parameter values (NPV) of each case scenario. Once the optimum scenario had been determined using the manual method (Deterministic Approach), the Stochastic Approach is conducted to further optimize the
field development plan with the objective functions.
A. Infill Wells Optimization (Deterministic Approach)
The addition of infill wells in this study was carried out by looking at the distribution of locations in the field with the Simulation Opportunity Index (SOI) that was calculated in the previous chapter. The addition of infill drilling wells was carried out one by one and the additional recovery factor (RF) and the cumulative oil production were observed as showed in Table 1.
Table 1
The Recovery Factor for Number of Infill Wells.
Scenario
Production Oil Rate @ 2041,
(STB/D)
Cumulative Production
Rate, (MMSTB)
Recovery Factor,
(%) Base Case
(6 Wells) 807 9.59 15.46%
Case 1
(+1 Infill Well) 686 10.82 17.46%
Case 2
(+2 Infill Wells) 662 12.00 19.35%
Case 3
(+3 Infill Wells) 606 12.83 20.69%
Case 4
(+4 Infill Wells) 546 13.24 21.36%
Case 5
(+5 Infill Wells) 494 13.66 22.03%
Case 6
(+6 Infill Wells) 463 13.97 22.54%
Case 7
(+7 Infill Wells) 421 14.08 22.71%
Case 8
(+8 Infill Wells) 397 14.10 22.74%
After the production forecasting simulation is carried out for 20 years, the result for the production profile of the cumulative oil, oil rate, and water production can be seen after increasing the number of infill wells in the Figures 7 and 8.
Figure 7
Cumulative Oil Vs Time (Infill Wells).
SWLWater saturation 1.00000 0.90000 0.80000 0.70000 0.60000 0.50000 0.40000 0.30000 0.20000 0.10000 0.00000
1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00
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RESULtS AND DISCUSSION
“AR” field development plan optimization was carried out starting with the determination of the optimum number of infill wells. Afterward, steam injection scenarios were added with a different number of injection wells. Then an economic calculation is carried out that will produce the economic parameter values (NPV) of each case scenario. Once the optimum scenario had been determined using the manual method (deterministic approach), the stochastic approach is conducted to further optimize the field development plan using the objective functions.
A. Infill Wells Optimization (Deterministic Approach)
The addition of infill wells in this study was carried out by looking at the distribution of locations in the field with the simulation opportunity index (SOI) that was calculated in the previous chapter.
The addition of infill drilling wells was carried out one by one and the additional recovery factor (RF) and the cumulative oil production were observed as showed in Table 1.
After the production forecasting for 20 years, the result for the production profile of the cumulative oil, oil rate, and water production can be seen after increasing the number of infill wells as shown on the Figures 7 and 8.
The addition of every infill well resulted in an increase in cumulative production and recovery factor. Based on the results of forecasting the production of infill well cases, the optimal case was chosen to be applied in “AR” field, is the case with the addition of five infill drilling wells. This can be seen from the addition of six infill drilling wells which resulted in cumulative oil production not being too significant compared to the case of adding five infill drilling wells.
B. Injection Wells Optimization Scenarios (Deterministic Approach)
The addition of injection wells in this study was carried out by converting the existing production wells in the field into steam injection wells and then manually analyze the obtained result. The conversion of injection wells was carried out on the existing
Table 1
The recovery factor for number of infill wells Oil production rate @
2041, Cumulative production
rate, Recovery factor,
(STB/D) (MMSTB) (%)
Base Case (6 wells)
Case 1 (+1 infill well)
Case 2 (+2 infill wells)
Case 3 (+3 infill wells)
Case 4 (+4 infill wells)
Case 5 (+5 infill wells)
Case 6 (+6 infill wells)
Case 7 (+7 infill wells)
Case 8
(+8 infill wells) 397 14.1 22.74%
463 13.97 22.54%
421 14.08 22.71%
546 13.24 21.36%
494 13.66 22.03%
662 12 19.35%
606 12.83 20.69%
Scenario
807 9.59 15.46%
686 10.82 17.46%
191 Feasibility Study and Technical Optimization by Implementing Steam Flooding for the Field
Development Plan of A Heavy-Oil Field in Yemen (Mohammed Sheikh Salem Al-Attas and Amega Yasutra)
Figure 7
Cumulative oil vs time (infill wells).
Figure 8
Oil recovery factor vs number of infill wells.
wells and infill wells one by one and additional recovery factor (RF) and the cumulative oil production were observed. Results are showed in Table 2.
The results for the production profile of cumulative oil, oil rate, and water output by converting some
infill wells into injection wells can be shown in Figures 9 and 10.
The addition of every injection well resulted in an increase in cumulative production and recovery factor. Based on the results of forecasting the
Title of Your Paper, First Author et al. / Scientific Contributions Oil and Gas
Figure 5
Trial and Error Simulation Method.
Simulation Opportunity Index (SOI) Analysis The Simulation Opportunity Index method was applied For field "X" using the Petrel simulator. The result of the SOI evaluation and the proposed infill well candidates are shown in Figure 6. The red zone represents the area with the highest oil potential and is recommended to drill in those zones. The Infill wells were selected from the previous POD plan location and those wells are also planned to be used for future injection.
Figure 6
Average Map (SOI) for Infill Wells.
IV. RESULTS AND DISCUSSION
Field “X” development plan optimization was carried out starting with the determination of the optimum number of infill wells. Afterward, steam injection scenarios were added with a different number of injection wells. Then an economic calculation is carried out that will produce the economic parameter values (NPV) of each case scenario. Once the optimum scenario had been determined using the manual method (Deterministic Approach), the Stochastic Approach is conducted to further optimize the
field development plan with the objective functions.
A. Infill Wells Optimization (Deterministic Approach)
The addition of infill wells in this study was carried out by looking at the distribution of locations in the field with the Simulation Opportunity Index (SOI) that was calculated in the previous chapter. The addition of infill drilling wells was carried out one by one and the additional recovery factor (RF) and the cumulative oil production were observed as showed in Table 1.
Table 1
The Recovery Factor for Number of Infill Wells.
Scenario
Production Oil Rate @ 2041,
(STB/D)
Cumulative Production
Rate, (MMSTB)
Recovery Factor,
(%) Base Case
(6 Wells) 807 9.59 15.46%
Case 1
(+1 Infill Well) 686 10.82 17.46%
Case 2
(+2 Infill Wells) 662 12.00 19.35%
Case 3
(+3 Infill Wells) 606 12.83 20.69%
Case 4
(+4 Infill Wells) 546 13.24 21.36%
Case 5
(+5 Infill Wells) 494 13.66 22.03%
Case 6
(+6 Infill Wells) 463 13.97 22.54%
Case 7
(+7 Infill Wells) 421 14.08 22.71%
Case 8
(+8 Infill Wells) 397 14.10 22.74%
After the production forecasting simulation is carried out for 20 years, the result for the production profile of the cumulative oil, oil rate, and water production can be seen after increasing the number of infill wells in the Figures 7 and 8.
Figure 7
Cumulative Oil Vs Time (Infill Wells).
Cumulative oil SC case_00.irf Cumulative oil SC case_01.irf Cumulative oil SC case_02.irf Cumulative oil SC case_03.irf Cumulative oil SC case_04.irf Cumulative oil SC case_05.irf Cumulative oil SC case_06.irf Cumulative oil SC case_07.irf Cumulative oil SC case_08.irf
2025 2030 2035 2040
Time (Date) 0,00e+0
2,00e+6 4,00e+6 6,00e+6 8,00e+6 1,00e+7 1,20e+7 1,40e+7
Cumulative oil SC (bbl)
First Author et al. / Mechatronics, Electrical Power, and Vehicular Technology XX (20XX) XX-XX
Figure 8
Oil Recovery Factor Vs Number of Infill Wells.
B. Injection Wells Optimization Scenarios (Deterministic Approach).
The addition of injection wells in this study was carried out by converting the existing production wells in the field into a steam injection well and manually analyze the result obtained. The conversion of injection wells was carried out on the existing wells and infill wells one by one and the additional recovery factor (RF) and the cumulative oil production were observed as showed in Table 2.
Table 2
Recovery Factor For Number Of Injection Wells.
Scenario
Cumulative Production
Rate, (MMSTB)
Recovery Factor,
(%)
Recovery Factor Inside Pattern Only, Do Nothing Case (6 (%)
Wells) 8.25 13.75% 27.50%
Base Case
(+ 6 Infill Wells) 9.60 16.00% 31.99%
Case 1
(+1 Converted Well) 12.71 21.18% 42.35%
Case 2
(+2 Converted Wells) 15.70 26.17% 52.34%
Case 3
(+3 Converted Wells) 17.77 29.61% 59.22%
Case 4
(+4 Converted Wells) 19.17 31.95% 63.89%
After the production forecasting simulation is carried out for 20 years, the result for the production profile of the cumulative oil, oil rate, and water production can be seen after increasing the number of infill wells in the Figures 9 and 10.
Figure 9
Cumulative Oil Vs Time (Injection Wells).
Figure 10
Oil Recovery Factor Vs Number of Injection Wells.
C. CMOST Using Maximum Recovery Factor (RF) Objective Function.
In this study, CMOST is used with the maximum Recovery Factor (RF) as an objective function to perform the optimization on the optimum injection well case obtained from the deterministic approach result. CMOST is used to optimize the location of the infill and injection wells as well as optimizing the steam injection rate, steam injection pressure, oil production rate and the well conversion. The setting used for the CMOST field optimization is shown in Table 3.
Table 3
CMOST Engine Settings and Optimization Parameters (Max Recovery Factor).
CMOST Engine SETTING
Study Type Optimization
Sampling method CMG DECE
Optimization Engine DECE
Number of Experiments 309
Objective Function Oil Cumulative, Oil Production Rate,
Net present Value Result Obtained End of Simulation Experiment Duration 6 Days
Parameters Min Max
Infill Wells Location -4 +4
Injection Wells Location -2 +2 Well Status Conversion INFILL INJ Steam Injection Rate, M3/D 100 325 Oil Production Rate M3/D 300 6000
The figures below are obtained from the CMOST Optimization for Field “X” development with the Maximum Cumulative Oil as the objective function.
The Experiment ID in Figure 11 showed that the CMOST optimization needed around 100 experiments to finally conduct the optimization after the sampling of the parameter had been analyzed.
23.00%
22.00%
21.00%
20.00%
19.00%
18.00%
17.00%
16.00%
15.00%
Recovery Factor %
0 Infill 1 Infill 2 Infill 3 Infill 4 Infill 5 Infill 6 Infill 7 Infill 8 Infill 9 Infill Infill Wells
192
Scientific Contributions Oil & Gas, Vol. 44. No. 3, December 2021: 183 - 198
production of injection well cases, the optimal case was chosen to be applied in “AR” field, is the case with the conversion of two infill wells into injection wells. This can be seen from the conversion of three infill drilling wells into injection wells which resulted in cumulative oil production not being too significant compared to the case of converting two infill wells.
Table 2
Recovery factor for number of injection wells
Figure 9
Cumulative oil vs time (injection wells).
C. CMOSt Using Maximum Recovery Factor (RF) Objective Function
In this study, CMOST is used with the maximum recovery factor (RF) as an objective function to perform the optimization on the optimum injection well case obtained from the deterministic approach result. CMOST is used to optimize the location of
Cumulative production
rate, Recovery factor, Recovery factor inside pattern only,
(MMSTB) (%) (%)
Do Nothing Case (6 wells) 8.25 13.75% 27.50%
Base Case (+ 6 infill wells)
Case 1 (+1 converted well)
Case 2 (+2 converted wells)
Case 3 (+3 converted wells)
Case 4
(+4 converted wells) 19.17 31.95% 63.89%
15.7 26.17% 52.34%
17.77 29.61% 59.22%
Scenario
9.6 16.00% 31.99%
12.71 21.18% 42.35%
First Author et al. / Mechatronics, Electrical Power, and Vehicular Technology XX (20XX) XX-XX
Figure 8
Oil Recovery Factor Vs Number of Infill Wells.
B. Injection Wells Optimization Scenarios (Deterministic Approach).
The addition of injection wells in this study was carried out by converting the existing production wells in the field into a steam injection well and manually analyze the result obtained. The conversion of injection wells was carried out on the existing wells and infill wells one by one and the additional recovery factor (RF) and the cumulative oil production were observed as showed in Table 2.
Table 2
Recovery Factor For Number Of Injection Wells.
Scenario
Cumulative Production
Rate, (MMSTB)
Recovery Factor,
(%)
Recovery Factor Inside Pattern Only, Do Nothing Case (6 (%)
Wells) 8.25 13.75% 27.50%
Base Case
(+ 6 Infill Wells) 9.60 16.00% 31.99%
Case 1
(+1 Converted Well) 12.71 21.18% 42.35%
Case 2
(+2 Converted Wells) 15.70 26.17% 52.34%
Case 3
(+3 Converted Wells) 17.77 29.61% 59.22%
Case 4
(+4 Converted Wells) 19.17 31.95% 63.89%
After the production forecasting simulation is carried out for 20 years, the result for the production profile of the cumulative oil, oil rate, and water production can be seen after increasing the number of infill wells in the Figures 9 and 10.
Figure 9
Cumulative Oil Vs Time (Injection Wells).
Figure 10
Oil Recovery Factor Vs Number of Injection Wells.
C. CMOST Using Maximum Recovery Factor (RF) Objective Function.
In this study, CMOST is used with the maximum Recovery Factor (RF) as an objective function to perform the optimization on the optimum injection well case obtained from the deterministic approach result. CMOST is used to optimize the location of the infill and injection wells as well as optimizing the steam injection rate, steam injection pressure, oil production rate and the well conversion. The setting used for the CMOST field optimization is shown in Table 3.
Table 3
CMOST Engine Settings and Optimization Parameters (Max Recovery Factor).
CMOST Engine SETTING
Study Type Optimization Sampling method CMG DECE Optimization Engine DECE Number of Experiments 309
Objective Function Oil Cumulative, Oil Production Rate,
Net present Value Result Obtained End of Simulation Experiment Duration 6 Days
Parameters Min Max
Infill Wells Location -4 +4 Injection Wells Location -2 +2
Well Status Conversion INFILL INJ Steam Injection Rate, M3/D 100 325 Oil Production Rate M3/D 300 6000
The figures below are obtained from the CMOST Optimization for Field “X” development with the Maximum Cumulative Oil as the objective function.
The Experiment ID in Figure 11 showed that the CMOST optimization needed around 100 experiments to finally conduct the optimization after the sampling of the parameter had been analyzed.
2025 2030 2035 2040
Time (Date) 0,00e+0
5,00e+6 1,00e+7 1,50e+7 2,00e+7
Cumulative Oil (bbl)
Cumulative oil SC case_00.irf Cumulative oil SC case_01.irf Cumulative oil SC case_02.irf Cumulative oil SC case_03.irf Cumulative oil SC case_04.irf
193 Feasibility Study and Technical Optimization by Implementing Steam Flooding for the Field
Development Plan of A Heavy-Oil Field in Yemen (Mohammed Sheikh Salem Al-Attas and Amega Yasutra)
Figure 10
Oil recovery factor vs Number of injection wells.
the infill and injection wells as well as optimizing the steam injection rate, steam injection pressure, oil production rate, and the well conversion. The setting used for the CMOST field optimization is shown in Table 3.
Figures 11 and 12 are obtained from the CMOST optimization for “AR” field development with maximum cumulative oil as the objective function.
The experiment ID in Figure 11 showed that the CMOST optimization needed around 150 experiments
to finally reach the optimization after the sampling of the parameter had been analyzed.
D. CMOSt Using Maximum Net Present Value (NPV) Objective Function
In this study, CMOST is also used with the maximum net present value (NPV) as an objective function to perform the optimization on the optimum injection well case obtained from the deterministic approach. CMOST is used to optimize the location
Table 3
CMOST engine settings and optimization parameters (max recovery factor)
Study Type Optimization
Sampling method CMG DECE
Optimization Engine DECE
Number of Experiments 309
Oil Cumulative, Oil Production Rate,
Net present Value
Result Obtained End of Simulation
Experiment Duration 6 Days
Parameters Min Max
Infill Wells Location -4 4
Injection Wells Location -2 2
Well Status Conversion INFILL INJ
Steam Injection Rate, M3/D 100 325
Oil Production Rate M3/D 300 6000
CMOST Engine Setting
Objective Function
First Author et al. / Mechatronics, Electrical Power, and Vehicular Technology XX (20XX) XX-XX
Figure 8
Oil Recovery Factor Vs Number of Infill Wells.
B. Injection Wells Optimization Scenarios (Deterministic Approach).
The addition of injection wells in this study was carried out by converting the existing production wells in the field into a steam injection well and manually analyze the result obtained. The conversion of injection wells was carried out on the existing wells and infill wells one by one and the additional recovery factor (RF) and the cumulative oil production were observed as showed in Table 2.
Table 2
Recovery Factor For Number Of Injection Wells.
Scenario
Cumulative Production
Rate, (MMSTB)
Recovery Factor,
(%)
Recovery Factor Inside Pattern Only, Do Nothing Case (6 (%)
Wells) 8.25 13.75% 27.50%
Base Case
(+ 6 Infill Wells) 9.60 16.00% 31.99%
Case 1
(+1 Converted Well) 12.71 21.18% 42.35%
Case 2
(+2 Converted Wells) 15.70 26.17% 52.34%
Case 3
(+3 Converted Wells) 17.77 29.61% 59.22%
Case 4
(+4 Converted Wells) 19.17 31.95% 63.89%
After the production forecasting simulation is carried out for 20 years, the result for the production profile of the cumulative oil, oil rate, and water production can be seen after increasing the number of infill wells in the Figures 9 and 10.
Figure 9
Cumulative Oil Vs Time (Injection Wells).
Figure 10
Oil Recovery Factor Vs Number of Injection Wells.
C. CMOST Using Maximum Recovery Factor (RF) Objective Function.
In this study, CMOST is used with the maximum Recovery Factor (RF) as an objective function to perform the optimization on the optimum injection well case obtained from the deterministic approach result. CMOST is used to optimize the location of the infill and injection wells as well as optimizing the steam injection rate, steam injection pressure, oil production rate and the well conversion. The setting used for the CMOST field optimization is shown in Table 3.
Table 3
CMOST Engine Settings and Optimization Parameters (Max Recovery Factor).
CMOST Engine SETTING
Study Type Optimization
Sampling method CMG DECE
Optimization Engine DECE
Number of Experiments 309
Objective Function Oil Cumulative, Oil Production Rate,
Net present Value Result Obtained End of Simulation Experiment Duration 6 Days
Parameters Min Max
Infill Wells Location -4 +4
Injection Wells Location -2 +2 Well Status Conversion INFILL INJ Steam Injection Rate, M3/D 100 325 Oil Production Rate M3/D 300 6000
The figures below are obtained from the CMOST Optimization for Field “X” development with the Maximum Cumulative Oil as the objective function.
The Experiment ID in Figure 11 showed that the CMOST optimization needed around 100 experiments to finally conduct the optimization after the sampling of the parameter had been analyzed.
O Injection 1 Injection 2 Injection 3 Injection 4 Injection Injection Wells
21,00%
22,00%
23,00%
24,00%
25,00%
26,00%
27,00%
28,00%
29,00%
Recovery Factor %
194
Scientific Contributions Oil & Gas, Vol. 44. No. 3, December 2021: 183 - 198
of the infill and injection wells as well as optimizing the steam injection rate, steam injection pressure, oil production rate and the well conversion. The setting used for the CMOST field optimization is shown in Table 4.
The CMOST optimization for “AR” field development with the Maximum net present value as the objective function are shown on Figures 13 and 14. After the parameter sampling was observed, the CMOST optimization required around 200
Figure 11
CMOST run progress experiment ID vs production cummulative objective function.
Figure 12
CMOST observed time for cummulative production objective function.
Title of Your Paper, First Author et al. / Scientific Contributions Oil and Gas
Figure 11
CMOST Run Progress Experiment ID Vs NP objective function.
Figure 12
CMOST Observed Time for NP objective function.
D. CMOST Using Maximum Net Present Value (NPV) Objective Function.
In this study, CMOST is used with the maximum Net Present Value (NPV) as an objective function to perform the optimization on the optimum injection well case obtained from the deterministic approach result. CMOST is used to optimize the location of the infill and injection wells as well as optimizing the steam injection rate, steam injection pressure, oil production rate and the well conversion. The setting used for the CMOST field optimization is shown in Table V.4.
Table 4
CMOST Engine Settings and Optimization Parameters (Max NPV).
CMOST Engine SETTING
Study Type Optimization
Sampling method CMG DECE
Optimization Engine DECE
Number of Experiments 306
Objective Function Net present Value Oil Cumulative, Oil Production Rate, Result Obtained End of Simulation Experiment Duration 5 Days
Parameters Min Max
Infill Wells Location -4 +4 Injection Wells Location -2 +2 Well Status Conversion INFILL INJ Steam Injection Rate, M3/D 100 325
Oil Production Rate M3/D 300 600
The figures below are obtained from the CMOST Optimization for Field “X” development with the Maximum Net Present Value as the objective function.
The Experiment ID in Figure 13 showed that the CMOST optimization needed around 100 experiments to finally conduct the optimization after the sampling of the parameter had been analyzed.
Figure 13
CMOST Run Progress Experiment ID Vs NPV objective function.
Figure 14
CMOST Observed Time for NPV objective function.
E. Final Optimization Result
For the purposes of this study the recovery factor was calculated only for the areas that has been places with production and injection (OOIP of 30 MMSTB) to see the effect of the steam injection and its pattern. The Field development optimization using CMOST as the objective function resulted in an increase in the oil recovery factor as well as the Net Present Value.
Cumulative Oil (m3)
3.2E+006 3.0E+006 2.8E+006 2.6E+006 2.4E+006 2.2E+006 2.0E+006 1.8E+006 1.6E+006 1.4E+006 1.2E+006 1.0E+006
0 50 100 150 200 250 300
Experiment ID
Base Case General Solutions Optimal Solutions
Title of Your Paper, First Author et al. / Scientific Contributions Oil and Gas
Figure 11
CMOST Run Progress Experiment ID Vs NP objective function.
Figure 12
CMOST Observed Time for NP objective function.
D. CMOST Using Maximum Net Present Value (NPV) Objective Function.
In this study, CMOST is used with the maximum Net Present Value (NPV) as an objective function to perform the optimization on the optimum injection well case obtained from the deterministic approach result. CMOST is used to optimize the location of the infill and injection wells as well as optimizing the steam injection rate, steam injection pressure, oil production rate and the well conversion. The setting used for the CMOST field optimization is shown in Table V.4.
Table 4
CMOST Engine Settings and Optimization Parameters (Max NPV).
CMOST Engine SETTING
Study Type Optimization
Sampling method CMG DECE
Optimization Engine DECE
Number of Experiments 306
Objective Function Net present Value Oil Cumulative, Oil Production Rate, Result Obtained End of Simulation Experiment Duration 5 Days
Parameters Min Max
Infill Wells Location -4 +4 Injection Wells Location -2 +2 Well Status Conversion INFILL INJ Steam Injection Rate, M3/D 100 325
Oil Production Rate M3/D 300 600
The figures below are obtained from the CMOST Optimization for Field “X” development with the Maximum Net Present Value as the objective function.
The Experiment ID in Figure 13 showed that the CMOST optimization needed around 100 experiments to finally conduct the optimization after the sampling of the parameter had been analyzed.
Figure 13
CMOST Run Progress Experiment ID Vs NPV objective function.
Figure 14
CMOST Observed Time for NPV objective function.
E. Final Optimization Result
For the purposes of this study the recovery factor was calculated only for the areas that has been places with production and injection (OOIP of 30 MMSTB) to see the effect of the steam injection and its pattern. The Field development optimization using CMOST as the objective function resulted in an increase in the oil recovery factor as well as the Net Present Value.
0.0E+000 5.0E+005 1.0E+006 1.5E+006 2.0E+006 2.5E+006 3.0E+006 3.5E+006
Entire Field Oil Prod Cum SCTR (m3)
2025 2030 2035 2040
Time
General Solutions Optimal Solutions Base Case
